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With Mipar, use Artificial Intelligence to analyze and process your images

Experts around the world trust Mipar to analyze their images. With the integration of artificial intelligence, Mipar is among the most powerful tools on the market. Moreover, it automates your analyses and is adapted to all fields that involve imaging.

5 good reasons to use this software

  • Intuitive to deploy and use
  • The most powerful image analysis software on the market, integrating Deep Learning
  • Automation thanks to predefined parametric files
  • Adaptation to all fields of application and all types of images
  • Intuitive report generation

These recipes perfectly match the images i need to analyze …. MIPAR has definitely made my life easier.

Material Sciences

Why choose Mipar?

Use Mipar to detect and measure features:

  1. Much faster and with much less variability in use than with manual analysis
  2. With greater accuracy than other automated solutions
  3. With built-in oversight of your results

Able to identify and measure the characteristics of all types and formats of images:

  • Microscopes
  • Scanners and radiographers
  • Satellites
  • Cameras
  • Smartphones
  • Drones

For students and universities

MIPAR is used by universities around the world to train students in advanced image analysis tools for materials science, life science, etc. Students have access to affordable prices on a monthly or semester basis. Departmental and university licenses are also available. If you are interested, we invite you to send us a quote request.

The MIPAR Academy is a comprehensive self-study course designed to get users up and running quickly while building lasting skills and expertise.

Post your questions and comments on the MIPAR User Forum to get answers from community members and MIPAR application experts.

Student Use Condition:

For use only by students on student-owned systems for academic courses and research at degree-granting institutions.

This offering is not for commercial, government or other organizational use.

If you wish to use MIPAR in a business, government lab, as an instructor at a university, for research, or for commercial or industrial purposes, please request a quote.

License option*: fixed seat, specific computer.

*The computer must be owned by the student, not the university.

Domains of application

Life sciences 

mitochondria imaging


Mitochondrial distribution in a differentiated PC12 cell: Fully automatic detection of mitochondria, separation of neurites and cell body, and measurement of mitochondrial fraction in each.

HE stained image


H&E staining of odontogenic keratocystic tumor epithelium: Quantification of epithelial layer thickness, after fully automated layer identification.

images of bacteria in petri dishes

Petri dishes

Bacterial colonies on a petri dish, automatic plate detection, estimation of the number of colonies that have grown together.

Sciences des matériaux

nanoparticle imaging analysis

Silica nanoparticle size and shape

Fully automated solution for nanoparticle size and shape characterization. Quantify a set of samples from a single scan or batch process.

nanofiber porosity analysis

Analysis of porosity and thickness of nanofibers

Quantify fiber density, void porosity and fiber thickness distribution.

crack failure analysis

Acrack analysis, part defect identification

Quantify additive manufacturing crack density, size distribution, localized density. Fully automated solution for single field or whole surface point quantification.

Inspection et contrôle

drone image analysis

Automated inspection of railroads using drone imagery

Track components quantified from drone images as part of a weekly railroad inspection mandated by the Federal Railroad Administration (FRA).

image of deforestation

Aerial study of the severity of deforestation

Percentage of forested (and deforested) areas automatically quantified from analysis of imagery produced by drones.

image contaminant control

Contaminant control

Classification and size measurement of filter paper contaminants: The detected contaminants are classified into predefined types and the size and shape are reported.


Deep Learning

  1. Trace : Trace your features on some images. Even use the plots you already have.
  2. Train : A deep neural network learns to recognize what you have plotted.
  3. Run : Run your model on a new image to detect complex features.

The features of Deep Learning: 

  • Adjustment of certain characters (e.g.: specific delimitation).
  • 5 to 10 image analyses are enough for learning and recognition.
  • From pre-established parametric forms followed by a recording 

Example usage

Detecting kernels while ignoring twins has been a challenge for the community for decades. Mipar’s Deep Learning function has successfully met this challenge.

MIPAR – Deep Learning Demo

grain analysis deep learning
Model trained on 25 images in 40 minutes on a GPU – Applied to a new image in 2 seconds.

Graphic display

  • Visual exploration of live image quantification. 
  • Exporting image data, graphs or digital tables ( individual or report)


Batch processing

  • Ability to batch analyze quickly and accurately.
  • Ability to make corrections if necessary.
  • Graphic visualization in the form of a histogram of the analyzed batch.
Screenshot of the batch processor that reveals the layout and purpose of each user interface element.

Here’s how to set up and run a batch process to segment and measure features from a set of images.

MIPAR – TUTORIAL Managing a 2D batch

Required configuration

The MIPAR API allows executing recipes in Python scripts and applications. Access to the API is granted to all perpetual licenses at no cost. Working in conjunction with MIPAR image analysis software, users can prototype, refine and deploy scalable image analysis solutions. The library can be deployed both locally and over a network.

Network deployment

The MIPAR API can be deployed on a network of servers as part of other networked applications, such as a networked database. Images stored in a database can be automatically quantified and archived.

How does this work?

  1. Upload the recipe you want.
  2. Open your image in the image processor.
  3. Pull and drop your recipe.
Starting from (excl tax)
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